1. Field of the Invention
The present invention relates to an image processing apparatus and a program storage medium for processing time-series images that include a plurality of images captured in time series.
2. Description of the Related Art
In recent years, a medical device as represented by a capsule endoscope, which sequentially captures images inside an intra-body canal such as an alimentary canal while moving inside the canal, has been developed. A capsule endoscope is swallowed from a mouth, is carried into a canal by using a peristalsis or the like, sequentially captures images at a predetermined capturing rate, transmits the captured images to an outside-body receiver, and is finally excreted outside the body. The number of captured time-series images is generally computed by a capturing rate (about 2 to 4 frames/sec) multiplied by an intra-body stay time (about 8 hours=8*60*60 seconds) of the capsule endoscope. The number of images is tens of thousands of sheets or more. A doctor spends a lot of time to check a great number of time-series images transmitted through the outside-body receiver by using a workstation for diagnosis or the like and specify a lesioned part. Thus, there is strongly desired a technology for improving the efficiency of the checking work of images performed by a doctor.
For example, Japanese Laid-open Patent Publication No. 2007-175432 discloses a method that divides time-series images captured by a capsule endoscope into multiple areas, such as mucous membrane, excrement, foam, and uncertainty. The method determines images that are unnecessary for observation and determines images that contain lesioned parts. Specifically, according to the method, each of images obtained by sampling time-series images at some intervals is divided into small sections, the small sections are mapped to a predetermined feature space such as an average color ratio space, and the mapped small sections are divided into clusters. Then, obtained clusters are classified into classes (categories) such as mucous membrane, excrement, and foam by using a classifier that is previously created on the basis of teacher data. Subsequently, the sampled images are divided into time-series-interval subsets. Occurrence frequency and distribution information of each class in each subset are calculated on the basis of the class classification result. Then, performed is classification, in the image in the time-series interval indicated by each subset, into class areas to detect a lesioned part on the basis of the calculation result.